AI Engineer – Python, LLM/GenAI, RAG, AWS & AI Agents

Job Summary Synechron is seeking an AI Engineer to build and implement AI agents and AI architecture within CI/CD workflows on AWS. The role will focus on developing production-ready generative AI solutions, retrieval-augmented generation (RAG) pipelines, agentic workflows, model integrations, and AI-enabled automation. The AI Engineer will contribute to the design, development, deployment, and continuous improvement of AI solutions using Python, large language models (LLMs), AWS services, vector databases, and secure engineering practices. The role will also support code generation, code fixing, security automation, and remediation systems where applicable. The position contributes to business objectives by accelerating software delivery, improving engineering productivity, enabling secure AI adoption, automating technical processes, and delivering scalable AI capabilities through reliable cloud-based architectures. Software Requirements Required Software Skills Python: Strong professional experience in Python and AI development, including application design, API development, automation, testing, and production deployment. LLMs and Generative AI: Minimum 3 years of experience working with LLMs and GenAI, including OpenAI, Bedrock, or similar technologies. RAG pipeline engineering: Hands-on experience designing and implementing RAG pipelines using SageMaker, Lambda, vector databases, embeddings, document processing, retrieval, and response generation. MCP server development: Experience developing MCP servers and integrating tools, services, APIs, and data sources with AI applications. Agentic workflows: Experience building agentic workflows or autonomous AI agents, including task planning, tool use, orchestration, memory, workflow control, and response validation. Code generation and code fixing using LLMs: Exposure to using LLMs for code generation, code review, code transformation, defect identification, and code remediation. Vector databases: Experience with vector databases, embedding generation, similarity search, metadata filtering, indexing, and retrieval optimization. AWS services: Good knowledge of AWS services, including Lambda, ECS/EKS, S3, API Gateway, and SageMaker. Application security: Understanding of application security principles, common vulnerabilities, secure coding practices, identity and access controls, and protection of AI applications and data. CI/CD integration: Experience implementing or integrating AI agents and AI architecture within CI/CD workflows on AWS. Preferred Software Skills Exposure to CI/CD tools such as GitHub, Jenkins, Harness, or equivalent tools. Experience with security tools such as Snyk, SonarQube, or equivalent tools. Knowledge of containerization and orchestration using Docker and Kubernetes. Prior work in security automation or remediation systems. Experience with automated testing, code quality checks, vulnerability scanning, and deployment gates.